Recently, the Long Term Evolution (LTE) standard has been developed in the 3rd Generation Partnership Project (3GPP) as the future wireless communication system. Cell search is a basic procedure of any cellular communication system by which a mobile terminal finds a cell for potential connection to, and then decodes all the information required to register. This paper presents a review of the state of the art solutions and recent patents in the fields of cell search and synchronization for LTE and wireless systems. Moreover, a recently introduced innovative acquisition technique is illustrated and depicted in details. Performance analysis is conducted in comparison with the conventional state of the art solution, evaluating the probability of detection under different multi-path standard channels.
The contribution deals with the principles of programmed learning, specifically with implementing a new module to Learning Management System Moodle. This study activity allows the construction of a new question type. If a student does not answer correctly, the main question is substituted by a set of easier sub-questions. The order of these subquestions is set by means of question maps suggested by the teacher. All processes related to displaying, answering and evaluating the questions are managed by the Moodle question-answering system. The paper briefly introduces the principles of programmed learning and it describes the method of branch learning that is used in the module implementation. Next the methods and the used tools for the module implementation are presented. The question map as a core of the module is explained, its function is described and the cooperation with the question bank is clarified. The use of the module is explained in a simple example. To simplify the process of question generation and the set of subquestions with the same entry data, a specific automatic generator of questions is applied. Next the principle of this automatic generator of question is introduced, the example of generation process is presented and some results of generation are depicted. Finally the results of the new module tested by users are discussed and in the conclusion other possible features of the module are proposed in relation to the process of question generation and to the work with the generated questions along with the discussion of few patents.
By constructing a matrix-valued unbounded error-function, this paper develops and exploits a new type of recurrent neural networks, named as Zhang neural networks, for the time-varying Lyapunov matrix equation with accuracy and effectiveness. In general, a scalar-valued norm-based energy function is defined for the design and development of the conventional gradient-based neural networks, which could only solve the time-invariant matrix equation exactly. Comparison with some recent patents on the neural networks designed originally for the time-invariant problems solving, the patents relevant to Zhang neural networks is designed for the solution of time-varying problems based on the matrix/ vector-valued error function. An illustrative example substantiates that the presented Zhang neural networks can effectively solve such matrix equation with time-varying coefficients, while the conventional gradient-based neural networks could only approximately approach to the theoretical solution.
Digital watermarking technology remains an active area of study and development by a number of companies and institutions since mid ‘90s. This area fetches more and more attention as one of the key technology components for copyright protection, content management, and copy control of digital contents. This paper discusses the different patents procured by Digimarc Corporation during the period between March 2012 and June 2012. The basic principles, the core characteristics and the uniqueness of each patent are touched upon followed by a discussion on the most suitable application each patent serves. The paper is organized around the application types namely - content identification and management, content protection of image/video content, image quality control, document and image security, authentication of content and objects. The challenges and the potential future scopes of the field are also pointed out.
This study develops a power-managed method for mobile devices, which employs mobile power banks constructed on an Android-based power control system. Combined with self-developed power bank monitoring and control software, this study developed a multi-functional battery charging control system. The design also considers and integrates external electronic devices and mechanisms, and provides additional functions aside from phone charging to satisfy the needs of female users. Consequently, the system reduces the weight of bags when travelling or going out and simultaneously increases user convenience. Few patents associated with power manage method are discussed in this review.
With the development of the economy, the construction and real estate industries have gradually become basic industries related to the people's livelihood and they play an important role. Because of their special characteristics in investment and financing structure, construction and real estate companies are faced with enormous pressure of financial risk, which urgently needs some effective models to send out early warning signals before financial distress. This study uses back-propagation neural network (BP-NN) as the base learner and constructs two classifier ensemble models, BPNNAdaBoost and BPNN-Bagging, based on AdaBoost and Bagging ensemble learning methods, for financial distress prediction of construction and real estate companies. We collect financial ratio data of 85 construction and real estate samples that are publicly traded in the Shanghai Stock Exchange and the Shenzhen Stock Exchange of China to construct three data sets and carry out empirical experiments. Results of BPNN-AdaBoost and BPNN-Bagging models are comprehensively compared with those of the single BP-NN and the classical Z3-score model. It is indicated that the two classifier ensemble approaches significantly outperform the single PB-NN and the classical Z3-score model, and BPNN-AdaBoost is more suitable for short-term and medium-term FDP in one- or two-year advance and BPNN-Bagging is more appropriate for long-term FDP in three-year advance. Besides, related recent patents are also reviewed.
Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. However, the algorithm suffers from premature convergence, slow convergence rate and large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up TLBO is considered necessary. This paper introduces a modification to basic TLBO that enhances the convergence rate without compromising with the solution quality. The performance of modified TLBO (mTLBO) on a test of functions is compared with original TLBO and other popular evolutionary techniques such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Differential evolution (DE) etc. It is found that mTLBO requires less computational efforts to locate global optimal solution. Further, the proposed mTLBO is implemented for few well known benchmark data clustering problems and its performances compared with basic TLBO as well. Results reveal that mTLBO is able to effectively cluster data points with better cluster performance measures such as quantization errors, intra cluster and inter cluster distances compared to TLBO. This paper examines six patents on optimization using evolutionary approach and their applications to different engineering fields.
Over the next decade, cloud computing has a good chance of becoming a widely used technology. However, many challenges face the cloud to be overcome before the average user or business team will trust their vital information with a cloud server. Most of these challenges tie into developing sound security measures for the cloud. One of the largest security obstacles is how to defend against a Denial-of-Service (DOS) or Distributed Denial-of-Service (DDOS) attacks from taking down a cloud server. DOS attacks are nothing new; many strategies have been proposed and tested against DOS attacks on networks. However, none have been able to completely prevent DOS attacks. The search continues for an effective solution to keep data available to legitimate users who need it when the cloud network that stores that data is the target of a DOS attack. The method proposed (DOSBAD) in this paper will explain how effectively detecting the bandwidth limit of a cloud network and the bandwidth currently in use to know when a DOS is beginning along with the discussion of very few patents.